基于机器学习技术的印度COVID-19病例增长预测

Aindrila Saha, Vartika Mishra, S. K. Rath
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摘要

最近世界面临的最大健康挑战之一是由冠状病毒疾病SARS-CoV-2(或世界卫生组织正式命名的Covid-19)引起的大流行。为了在不久的将来规划某一地点的医疗设施,以对抗这种疾病,公共卫生政策制定者希望对该地点的Covid-19阳性病例数量进行可靠的预测。基于可靠预测的要求,需要研究过去新冠病毒阳性病例的增长情况,并预测近期的增长情况。在本研究中,使用几种基于机器学习的回归技术,即多元线性回归、决策树回归和支持向量回归,对Covid-19阳性病例数量的增长进行了建模。此外,还应用了基于Filter和Wrapper方法的不同特征选择技术来选择适合的特征进行预测。本研究提出了对一个地区近期新冠肺炎病例数增长模式建模的最佳观测方法,以及可用于获得最优特征集的最佳选择方法。研究发现,与其他回归模型相比,非正则化多元线性回归模型在测试数据集上预测未来新冠肺炎病例数的效果较好,而向后消除特征选择方法比其他特征选择方法效果更好。
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Prediction of growth in COVID-19 Cases in India based on Machine Learning Techniques
One of the biggest health challenges that the world has faced in recent times is the pandemic due to coronavirus disease known as SARS-CoV-2, or Covid-19 as officially named by the World Health Organization (WHO). To plan medical facilities in a certain location in order to combat the disease in near future, public health policy makers expect reliable prediction of the number of Covid-19 positive cases in that location. The requirement of reliable prediction gives rise to the need for studying growth in the number of Covid-19 positive cases in the past and predicting the growth in the number in near future. In this study, the growth in the number of Covid-19 positive cases have been modelled using several machine learning based regression techniques viz., Multiple Linear Regression, Decision Tree Regression and Support Vector Regression. Further, different feature selection techniques based on Filter and Wrapper methods have been applied to select the suitable features based on which prediction is to be done. This study proposes the best observed method for modelling the pattern of growth in number of Covid-19 cases in the near future for a locality and also the best selection method that can be employed for obtaining the optimal feature set. It has been observed that unregularized Multiple Linear regression model yields promising results on the test data set, compared to the other regression models, for predicting the future number of Covid-19 cases and Backward Elimination feature selection method performs better than other feature selection methods.
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